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Side-Aware Boundary Localization for More Precise Object Detection

Introduction

We provide config files to reproduce the object detection results in the ECCV 2020 Spotlight paper for Side-Aware Boundary Localization for More Precise Object Detection.

@inproceedings{Wang_2020_ECCV,
    title = {Side-Aware Boundary Localization for More Precise Object Detection},
    author = {Jiaqi Wang and Wenwei Zhang and Yuhang Cao and Kai Chen and Jiangmiao Pang and Tao Gong and Jianping Shi and Chen Change Loy and Dahua Lin},
    booktitle = {ECCV},
    year = {2020}
}

Results and Models

The results on COCO 2017 val is shown in the below table. (results on test-dev are usually slightly higher than val). Single-scale testing (1333x800) is adopted in all results.

Method Backbone Lr schd ms-train box AP Config Download
SABL Faster R-CNN R-50-FPN 1x N 39.9 config model | log
SABL Faster R-CNN R-101-FPN 1x N 41.7 config model | log
SABL Cascade R-CNN R-50-FPN 1x N 41.6 config model | log
SABL Cascade R-CNN R-101-FPN 1x N 43.0 config model | log
Method Backbone GN Lr schd ms-train box AP Config Download
SABL RetinaNet R-50-FPN N 1x N 37.7 config model | log
SABL RetinaNet R-50-FPN Y 1x N 38.8 config model | log
SABL RetinaNet R-101-FPN N 1x N 39.7 config model | log
SABL RetinaNet R-101-FPN Y 1x N 40.5 config model | log
SABL RetinaNet R-101-FPN Y 2x Y (640~800) 42.9 config model | log
SABL RetinaNet R-101-FPN Y 2x Y (480~960) 43.6 config model | log